Applications for cognitive systems roughly fall into three classes: (1) synthetic humans for simulation and training; (2) various augmented cognition solutions such as those employing representations of expert or user cognitive models; and (3) intelligent control processes including adaptive interfaces. In each case, a computational model(s) of human cognitive processes and or knowledge representation(s) is a basic ingredient to the cognitive system. Tools enabling development of cognitive systems, including the constituent models of cognitive processes, are a central feature of the proposed development environment.
To date, there has been limited application for human modeling and simulation technologies. In general, attention to visual graphics and computational algorithms has dominated concern for behavioral realism. Nonetheless, various camps have made good progress in the computational representation of basic cognitive processes. However, there has been marginal progress toward representations that incorporate the influence of organic factors on behavior (i.e., arousal, fatigue, etc.). It has been a goal to develop a comprehensive framework that encompasses the requisite cognitive processes, but also incorporates organic factors that range from the microscopic (e.g., metabolic, pharmacological, etc.) to the macroscopic (i.e., culture) in a parsimonious fashion.
Two earlier projects contributed to the initial conceptualizations for the human emulator. First, in developing the behavior model for a small unit combat simulator, we sought an instantiation of human naturalistic decision making theory within the context of an agent-based computer simulation. This instantiation utilized Klein's Recognition Primed Decision Making (RPD) model with emphasis on Level 1 decision making. (See G. Klein, “An overview of naturalistic decision making applications, in Naturalistic Decision Making, Lawrence Earlbaum, Mowah, NJ (1997), pp. 49-59.) According to RPD, an expert decision maker commits their resources to evaluating the situation and through this evaluation, patterns are detected that lead to recognition that the current situation is analogous to situations that are known from past experience. The appropriate course of action is implicit in recognition of the situation. The instantiation of this concept involved the representation of environmental cues and relevant knowledge in a manner that accommodates pattern recognition. Patterns are associated with known situations (i.e., tactics) and once there is a match between the ongoing situation and a known situation, generic scripts are employed to direct agent behavior.
Secondly, within a systems engineering/safety context, Forsythe and Wenner (2000) have advanced an organic model to account for human influences on engineered systems. (See C. Forsythe et al., “Surety of human elements of high consequence systems: An organic model”, Proceedings of the IEA 2000/HFES 2000 Congress, 3-839-3-842.) This model challenges physical and computational science-based approaches through its emphasis on an organic systems perspective. Specifically, it is asserted that due to human influences, engineered systems are inherently organic and will exhibit properties of organic systems. These properties have been described and provide a basis for predicting aberrant behavior of engineered systems.
Early in development, it was realized that a purely psychological model would be inadequate for representing the influence of organic factors on cognitive behavior. There is enormous ambiguity in basic terminology (e.g., stress, arousal) and without a representation of underlying mechanisms, the scope and predictive capabilities would be severely limited. However, many facets of cognitive behavior (e.g., knowledge representation) are well described by psychological models (See T. E. Goldsmith et.al., “Assessing structural knowledge”, Journal of Educational Psychology, 83(1), (1991), pp, 88-96.)
Consequently, this invention utilizes a two-tiered approach in which knowledge is represented using a psychological model, and a separate physiology-based model serves as the engine that drives a psychological model. The fact that knowledge is not directly represented in the neural (i.e., physiological) model distinguishes this design from neural net and connectionist approaches, yet facilitates representation of the vast quantities of knowledge essential to a realistic emulation.
The mapping of the psychological to the physiological model is critical. We retain the concepts embodied by Recognition Primed Decision Making. This includes a separate representation of individual situational elements, pattern recognition and activation of schema-like representation of known situations. Frame/Content theory provided an initial bridge. This theory asserts that the representation of individual elements of content within a structural or contextual frame is a basic organizing principle of the neural system (See P. MacNeilage, “The frame/content theory of evolution of speech production”, Behavioral and Brain Sciences, 21, (1998) pp. 499-546.) Examples include figure/ground relationships in perception, syntax and semantics in linguistics, and differential motor specialization for stabilization and manipulation. Applying frame/content theory, individual elements of a situation represent content, whereas situation schema provide an interpretive frame.
Further extension involves mapping these ideas to the model of memory processes proposed by Wolfgang Klimesch and colleagues. (See W. Klimesch, “Memory processes, brain oscillations and EEG synchronization”, International Journal of Psychophysiology, 24, (1996), pp. 61-100.) Two phenomena have been described. First, in the absence of intrinsic or extrinsic stimulation, regions associated with semantic memory exhibit synchronous activation in the high alpha (10-13 Hz) bandwidth. It is suggested that semantic memory processes involve the activation of numerous localized neural assemblies. These assemblies oscillate in phase with pulses from a pacemaker until stimulated, at which time activation increases and assemblies begin to oscillate independent of the pacemaker. At this point, there is desynchronization. In contrast, episodic processes exhibit a completely different profile. Specifically, processing demands lead to increased synchronization in the theta (4-7 Hz) bandwidth. This pattern of activation is consistent with oscillation of a single distributed neural assembly.
The cognition studies presented above relate to humans. This invention uses these teachings and other features in a computer model.
This approach to Cognitive Systems may be distinguished from common AI-based and expert systems due to an emphasis on human-like, non-deterministic situation recognition processes, as opposed to reasoning through logical and/or rule-based operations. A primary objective has been to attain a machine emulation of human naturalistic decision making, including the influence of individualized factual and experiential knowledge. These developments are believed essential to attaining more realistically human-like synthetic entities for simulation-based analysis and training. However, these capabilities also enable development of systems in which machines are endowed with human-like cognitive representations, allowing human-machine interactions to move a step closer to human-human interactions.